Analysis of application data from new students

Import data

dataset <- read.xlsx("application_data.xlsx","Sheet1",header=TRUE)

Inspect data structure

str(dataset)
## 'data.frame':    250 obs. of  5 variables:
##  $ Degree          : chr  "Bachelar" "Bachelar" "Ph. D." "Ph. D." ...
##  $ Department      : chr  "Department of Civil Engineering" "Department of Civil Engineering" "Department of Civil Engineering" "Department of Civil Engineering" ...
##  $ College         : chr  "College of Engineering" "College of Engineering" "College of Engineering" "College of Engineering" ...
##  $ Nationality     : chr  "Swaziland" "Canada" "Pakistan" "Ethiopia" ...
##  $ Admission.Status: chr  "admitted" "admitted" "admitted" "admitted" ...

Descriptive analysis

By nationality

dataset %>%
  count(Nationality, sort=TRUE) %>%
  mutate(Nationality=paste(Nationality,n,sep="\n")) %>%
  treemap(index="Nationality",
      vSize="n", title="Nationality"
  )

dataset %>%
  group_by(Nationality) %>%
  summarise(count = n()) %>%
  arrange(desc(count)) %>%
  mutate( proportion = count / sum(count) * 100 )
## # A tibble: 44 x 3
##    Nationality                      count proportion
##    <chr>                            <int>      <dbl>
##  1 Indonesia                           37       14.8
##  2 Vietnam                             30       12  
##  3 India                               25       10  
##  4 Pakistan                            19        7.6
##  5 Malaysia                            15        6  
##  6 Japan                               12        4.8
##  7 Thailand                            12        4.8
##  8 Belize                               8        3.2
##  9 Swaziland                            8        3.2
## 10 Turkey                               8        3.2
## 11 Mongolia                             6        2.4
## 12 Nepal                                6        2.4
## 13 Egypt                                5        2  
## 14 Ethiopia                             5        2  
## 15 Philippines                          5        2  
## 16 Haiti                                4        1.6
## 17 Saint Vincent and the Grenadines     4        1.6
## 18 South Korea                          4        1.6
## 19 Sri Lanka                            4        1.6
## 20 France                               3        1.2
## 21 Bangladesh                           2        0.8
## 22 Guatemala                            2        0.8
## 23 Paraguay                             2        0.8
## 24 Saint Lucia                          2        0.8
## 25 Somaliland                           2        0.8
## 26 United Kingdom                       2        0.8
## 27 Australia                            1        0.4
## 28 Benin                                1        0.4
## 29 Botswana                             1        0.4
## 30 Cambodia                             1        0.4
## 31 Canada                               1        0.4
## 32 Fiji                                 1        0.4
## 33 Honduras                             1        0.4
## 34 Hungary                              1        0.4
## 35 Kiribati                             1        0.4
## 36 Morocco                              1        0.4
## 37 North Korea                          1        0.4
## 38 Norway                               1        0.4
## 39 Palau                                1        0.4
## 40 Peru                                 1        0.4
## 41 Russia                               1        0.4
## 42 Slovenia                             1        0.4
## 43 Tanzania                             1        0.4
## 44 United States                        1        0.4

By college

dataset %>%
  count(College, sort=TRUE) %>%
  mutate(College=str_remove(College,"College of ")) %>%
  mutate(College=paste(College,n,sep="\n")) %>%
  treemap(index="College",
      vSize="n", title="College"
  )

dataset %>%
  group_by(College) %>%
  summarise(count = n()) %>%
  arrange(desc(count)) %>%
  mutate( proportion = count / sum(count) * 100 )
## # A tibble: 10 x 3
##    College                                                  count proportion
##    <chr>                                                    <int>      <dbl>
##  1 College of Agriculture and Natural Resources               102       40.8
##  2 College of Engineering                                      48       19.2
##  3 College of Management                                       42       16.8
##  4 College of Life Science                                     23        9.2
##  5 College of Liberal Arts                                     17        6.8
##  6 College of Veterinary Medicine                               7        2.8
##  7 College of Electrical Engineering and Computer Science       6        2.4
##  8 College of Law and Poitics                                   3        1.2
##  9 International College of Innovation and Industry Liaison     1        0.4
## 10 Multicollege                                                 1        0.4

By department

dataset %>%
  count(Department, sort=TRUE) %>%
  mutate(Department=str_remove(Department,"Department of ")) %>%
  mutate(Department=paste(Department,n,sep="\n")) %>%
  treemap(index="Department",
      vSize="n", title="Department"
  )

dataset %>%
  group_by(Department) %>%
  summarise(count = n()) %>%
  arrange(desc(count)) %>%
  mutate( proportion = count / sum(count) * 100 )
## # A tibble: 49 x 3
##    Department                                                   count proportion
##    <chr>                                                        <int>      <dbl>
##  1 International Bachelor Program of Agribusiness                  27       10.8
##  2 International Master Program of Agriculture                     26       10.4
##  3 Department of Marketing                                         20        8  
##  4 Graduate Institute of Biomedical Engineering                    14        5.6
##  5 Department of Mechanical Engineering                            13        5.2
##  6 Department of Foreign Languages and Literatures                 10        4  
##  7 Department of Animal Science                                     9        3.6
##  8 Department of Business Administration                            9        3.6
##  9 Department of Applied Economics                                  8        3.2
## 10 Department of Chemical Engineering                               7        2.8
## 11 Department of Physics                                            7        2.8
## 12 Department of Life Sciences                                      6        2.4
## 13 Department of Plant Pathology                                    6        2.4
## 14 Graduate Institute of Technology and Management                  6        2.4
## 15 Department of Electrical Engineering                             5        2  
## 16 Department of Veterinary Medicine                                5        2  
## 17 Department of Civil Engineering                                  4        1.6
## 18 Department of Enviromental Engineering                           4        1.6
## 19 Department of Materials Science and Engineering                  4        1.6
## 20 Department of Soil and Environmental Sciences                    4        1.6
## 21 Master Program for Agricultural Economics and Marketing          4        1.6
## 22 Department of Applied Mathematics                                3        1.2
## 23 Department of Chinese Literature                                 3        1.2
## 24 Department of Entomology                                         3        1.2
## 25 Department of Food Science and Biotechnology                     3        1.2
## 26 Department of Horticulture                                       3        1.2
## 27 Department of Management Information Systems                     3        1.2
## 28 Graduate Institute of Biotechnology                              3        1.2
## 29 Graduate Institute of International Politics                     3        1.2
## 30 Department of Accounting                                         2        0.8
## 31 Department of Agronomy                                           2        0.8
## 32 Department of Chemistry                                          2        0.8
## 33 Department of Forestry                                           2        0.8
## 34 Graduate Institute of Microbiology and Public Health             2        0.8
## 35 Graduate Institute of Precision Engineering                      2        0.8
## 36 Institute of Molecular Biology                                   2        0.8
## 37 International PhD Program in Taiwan and Transcultural Studi~     2        0.8
## 38 Department of Bio-Industrial Mechatronics Engineering            1        0.4
## 39 Department of Computer Science and Engineering                   1        0.4
## 40 Department of Finance                                            1        0.4
## 41 Department of History                                            1        0.4
## 42 Department of Soil and Water Conservation                        1        0.4
## 43 Graduate Institute of Biochemistry                               1        0.4
## 44 Graduate Institute of Library and Information Science            1        0.4
## 45 Graduate Institute of Sports and Health Management               1        0.4
## 46 Graduate Institute of Statistics                                 1        0.4
## 47 Institute of Genomics and Bioinfomatics                          1        0.4
## 48 Ph.D. Program in Tissue Engineering and Regenerative Medici~     1        0.4
## 49 Tricontinental Master Program in Global Studies                  1        0.4

By degree

dataset %>%
  count(Degree, sort=TRUE) %>%
  mutate(Degree=paste(Degree,n,sep="\n")) %>%
  treemap(index="Degree",
      vSize="n", title="Degree"
  )

dataset %>%
  group_by(Degree) %>%
  summarise(count = n()) %>%
  arrange(desc(count)) %>%
  mutate( proportion = count / sum(count) * 100 )
## # A tibble: 3 x 3
##   Degree   count proportion
##   <chr>    <int>      <dbl>
## 1 Master     103       41.2
## 2 Bachelar    75       30  
## 3 Ph. D.      72       28.8

Relation analysis

Relation between nationality and college

Relation between nationality and department

Relation between nationality and degree

Relation between department and degree